Abstract Mean-shift tracking algorithm is a widely used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel Mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity of target sub-block and... Mean-shift tracking algorithm is a widely used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel Mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity of target sub-block and candidate sub-block, also by the ratio of the target sub-block area and the overall area. At the same time, the target sub-block area is found by means of the narrow band level set combined with penalty to improve the extraction accuracy and operating efficiency. Both of the target region’s RGB color information and the pixel’s position information are taken into consideration while describing the feature model of target and candidate region inside each sub-block. Many experimental results demonstrate the successful tracking of targets with background change and shade during the dynamic scene, where the basic Mean-shift tracking algorithm fails. And the proposed method has better tracking performance with higher tracking accuracy and adaptability. Read More Read Less

Abstract Recently, there has been a growing interest in the usage of mobile services and applications for healthcare. However, this growth also brings on many challenges such as privacy preservation. Many research works have been carried out emphasizing the important role privacy policies play in protecting patients’ private data from any kind of violation or misuse. In fact, policies are expressed using natural languages... Recently, there has been a growing interest in the usage of mobile services and applications for healthcare. However, this growth also brings on many challenges such as privacy preservation. Many research works have been carried out emphasizing the important role privacy policies play in protecting patients’ private data from any kind of violation or misuse. In fact, policies are expressed using natural languages reflecting different actions third parties may perform on patient’s data. These policies may not necessary satisfy patients’ privacy preferences leading to conflicting situations. In this paper, we compare some privacy policies languages suggested in the literature taking into account a number of criteria such as high-expressiveness, abstraction and delegation of authority support. Also, we propose an approach aiming at resolving conflicts among privacy policies by negotiation. Finally, in order to show how our solution can be applied, we consider an example of conflicting privacy policies. For that, we adopt S4P, a language for specifying both patients’ preferences and third parties’ policies and which satisfy the different criteria considered in the comparative study. Read More Read Less

Abstract Mean-shift tracking algorithm is a widely used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel Mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity of target sub-block and... Mean-shift tracking algorithm is a widely used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel Mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity of target sub-block and candidate sub-block, also by the ratio of the target sub-block area and the overall area. At the same time, the target sub-block area is found by means of the narrow band level set combined with penalty to improve the extraction accuracy and operating efficiency. Both of the target region’s RGB color information and the pixel’s position information are taken into consideration while describing the feature model of target and candidate region inside each sub-block. Many experimental results demonstrate the successful tracking of targets with background change and shade during the dynamic scene, where the basic Mean-shift tracking algorithm fails. And the proposed method has better tracking performance with higher tracking accuracy and adaptability. Read More Read Less

Abstract Recently, there has been a growing interest in the usage of mobile services and applications for healthcare. However, this growth also brings on many challenges such as privacy preservation. Many research works have been carried out emphasizing the important role privacy policies play in protecting patients’ private data from any kind of violation or misuse. In fact, policies are expressed using natural languages... Recently, there has been a growing interest in the usage of mobile services and applications for healthcare. However, this growth also brings on many challenges such as privacy preservation. Many research works have been carried out emphasizing the important role privacy policies play in protecting patients’ private data from any kind of violation or misuse. In fact, policies are expressed using natural languages reflecting different actions third parties may perform on patient’s data. These policies may not necessary satisfy patients’ privacy preferences leading to conflicting situations. In this paper, we compare some privacy policies languages suggested in the literature taking into account a number of criteria such as high-expressiveness, abstraction and delegation of authority support. Also, we propose an approach aiming at resolving conflicts among privacy policies by negotiation. Finally, in order to show how our solution can be applied, we consider an example of conflicting privacy policies. For that, we adopt S4P, a language for specifying both patients’ preferences and third parties’ policies and which satisfy the different criteria considered in the comparative study. Read More Read Less